Mining Drug Properties for Decision Support in Dental Clinics

Goh, Wee Pheng; Tao, Xiaohui; Zhang, Ji; Yong, Jianming

doi:10.1007/978-3-319-57529-2_30

Cited by 2 publications

(3 citation statements)

References 15 publications

(17 reference statements)

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“…Moreover, the word embedding method is also adopted which uses features that relate the similarity of a drug-pair in terms of how closely the words are related to each drug of the drug-pair. This approach distinguishes from our earlier work where feature vectors were constructed based on term similarities within the drug corpus [5]. In the application that is demonstrated here, the prescription of the drug will take into account the current drug that the patient is taking and the drug that the patient is allergic to.…”

Section: Related Workmentioning

confidence: 99%

“…Research work has been done to observe the relationship between drug interactions and word embeddings from the textual data that describe the drugs [4,5]. As cited by Nguyen (2019) from linguist JR Firth that "you shall know a word by the company it keeps" [8], related words and hence similar drugs can be known by finding similar words that describe the drugs.…”

Section: Evolution Of Discovered Drug Interactionsmentioning

confidence: 99%

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Personalised Drug Prescription for Dental Clinics Using Word Embedding

Goh

Tao

Zhang

et al. 2020

Communications in Computer and Information Science

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The number of drugs in drug databases is constantly expanding with novel drugs appearing on the market each year. A dentist cannot be expected to recall all the drugs available, let alone potential drug-drug interactions (DDI). This can be problematic when dispensing drugs to patients especially those with multiple medical conditions who often take a multiple medications. Any new medication prescribed must be checked against the patient's medical history, in order to avoid drug allergies and reduce the risk of adverse reactions. Current drug databases allowing the dentist to check for DDI are limited by the lack of integration of the patient's medical profile with the drug to be prescribed. Hence, this paper introduces a software which predicts the possible DDI of a new medication against the patient's medical profile, based on previous findings that associate similarity ratio with DDI. This system is based conceptually on a three-tier framework consisting of a knowledge layer, prediction layer and presentation layer. The novel approach of this system in applying feature vectors for drug prescription will be demonstrated during the conference (http://r.glory.sg/main.php). By engaging with the interactive demonstration, participants will gain first-hand experience in the process from research idea to implementation. Future work includes the extension of use from dental to medical institutions, and it is currently being enhanced to serve as a training tool for medical students.

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Section: Related Workmentioning

confidence: 99%

Section: Evolution Of Discovered Drug Interactionsmentioning

confidence: 99%

Personalised Drug Prescription for Dental Clinics Using Word Embedding

Goh

Tao

Zhang

et al. 2020

Communications in Computer and Information Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Based on the assumption that similar drug-pairs have a higher similarity ratio than that of dissimilar pairs, the aim of this paper is to explain and evaluate a novel method in predicting if a drug-pair is similar. While the three-tier framework has been used in the extraction of feature vectors from drug attributes in our previous work [5], this study expands on this by describing the word embedding approach within the predictive layer in finding the similarity of a drug-pair. The text corpus is trained on Google's word2Vec platform where word embedding models are generated and used for the extraction of feature vectors.…”

Section: Introductionmentioning

confidence: 99%

Drug prescription support in dental clinics through drug corpus mining

Goh

Tao

et al. 2018

Int J Data Sci Anal

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The rapid increase in the volume and variety of data poses a challenge to safe drug prescription for the dentist. The increasing number of patients that take multiple drugs further exerts pressure on the dentist to make the right decision at point-of-care. Hence, a robust decision support system will enable dentists to make decisions on drug prescription quickly and accurately. Based on the assumption that similar drug-pairs have a higher similarity ratio, this paper suggests an innovative approach to obtain the similarity ratio between the drug that the dentist is going to prescribe and the drug that the patient is currently taking. We conducted experiments to obtain the similarity ratios of both positive and negative drug-pairs, by using feature vectors generated from term similarities and word embeddings of bio-medical text corpus. This model can be easily adapted and implemented for use in a dental clinic to assist the dentist in deciding if a drug is suitable for prescription, taking into consideration the medical profile of the patients. Experimental evaluation of our model's association of the similarity ratio between two drugs yielded a superior F score of 89%.Hence, such an approach, when integrated within the clinical work-flow, will reduce prescription errors and thereby increase the health outcomes of patients.

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Mining Drug Properties for Decision Support in Dental Clinics

Cited by 2 publications

References 15 publications

Personalised Drug Prescription for Dental Clinics Using Word Embedding

Personalised Drug Prescription for Dental Clinics Using Word Embedding

Drug prescription support in dental clinics through drug corpus mining

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